

import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import BeitImageProcessor







class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=7    ,						__UpperCAmelCase=3    ,						__UpperCAmelCase=18    ,						__UpperCAmelCase=30    ,						__UpperCAmelCase=4_00    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=[0.5, 0.5, 0.5]    ,						__UpperCAmelCase=[0.5, 0.5, 0.5]    ,						__UpperCAmelCase=False    ,						)		->       Optional[Any]:
														A      :					List[str]      		=					size if size is not None else {'''height''': 20, '''width''': 20}
														A      :					int      		=					crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
														A      :					List[str]      		=					parent
														A      :					Any      		=					batch_size
														A      :					Any      		=					num_channels
														A      :					Union[str, Any]      		=					image_size
														A      :					List[str]      		=					min_resolution
														A      :					Dict      		=					max_resolution
														A      :					Tuple      		=					do_resize
														A      :					List[str]      		=					size
														A      :					int      		=					do_center_crop
														A      :					Optional[int]      		=					crop_size
														A      :					Union[str, Any]      		=					do_normalize
														A      :					Optional[int]      		=					image_mean
														A      :					List[str]      		=					image_std
														A      :					str      		=					do_reduce_labels






							def       snake_case			(					self						)		->       Optional[int]:
														return {
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_center_crop": self.do_center_crop,
														    "crop_size": self.crop_size,
														    "do_normalize": self.do_normalize,
														    "image_mean": self.image_mean,
														    "image_std": self.image_std,
														    "do_reduce_labels": self.do_reduce_labels,
														}






def        snake_case__		(    ):
							A      :					Optional[Any]      		=					load_dataset('''hf-internal-testing/fixtures_ade20k'''     ,				split='''test'''		)

							A      :					int      		=					Image.open(dataset[0]['''file''']		)
							A      :					Optional[int]      		=					Image.open(dataset[1]['''file''']		)

							return image, map






def        snake_case__		(    ):
							A      :					int      		=					load_dataset('''hf-internal-testing/fixtures_ade20k'''     ,				split='''test'''		)

							A      :					int      		=					Image.open(ds[0]['''file''']		)
							A      :					Optional[int]      		=					Image.open(ds[1]['''file''']		)
							A      :					List[str]      		=					Image.open(ds[2]['''file''']		)
							A      :					List[str]      		=					Image.open(ds[3]['''file''']		)

							return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[Any]								=	BeitImageProcessor if is_vision_available() else None




							def       snake_case			(					self						)		->       int:
														A      :					Optional[int]      		=					BeitImageProcessingTester(self						)




							@property
							def       snake_case			(					self						)		->       Dict:
														return self.image_processor_tester.prepare_image_processor_dict()




							def       snake_case			(					self						)		->       List[str]:
														A      :					List[str]      		=					self.image_processing_class(**self.image_processor_dict						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''do_resize'''						)						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''size'''						)						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''do_center_crop'''						)						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''center_crop'''						)						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''do_normalize'''						)						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''image_mean'''						)						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''image_std'''						)						)




							def       snake_case			(					self						)		->       List[str]:
														A      :					Optional[int]      		=					self.image_processing_class.from_dict(self.image_processor_dict						)
														self.assertEqual(image_processor.size    ,						{'''height''': 20, '''width''': 20}						)
														self.assertEqual(image_processor.crop_size    ,						{'''height''': 18, '''width''': 18}						)
														self.assertEqual(image_processor.do_reduce_labels    ,						__UpperCAmelCase						)

														A      :					Tuple      		=					self.image_processing_class.from_dict(
														    self.image_processor_dict    ,						size=42    ,						crop_size=84    ,						reduce_labels=__UpperCAmelCase						)
														self.assertEqual(image_processor.size    ,						{'''height''': 42, '''width''': 42}						)
														self.assertEqual(image_processor.crop_size    ,						{'''height''': 84, '''width''': 84}						)
														self.assertEqual(image_processor.do_reduce_labels    ,						__UpperCAmelCase						)




							def       snake_case			(					self						)		->       Tuple:
														pass




							def       snake_case			(					self						)		->       List[str]:
														# Initialize image_processing
														A      :					Any      		=					self.image_processing_class(**self.image_processor_dict						)
														# create random PIL images
														A      :					Union[str, Any]      		=					prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=__UpperCAmelCase						)
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase    ,						Image.Image						)

														# Test not batched input
														A      :					Dict      		=					image_processing(image_inputs[0]    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )    ,						)

														# Test batched
														A      :					str      		=					image_processing(__UpperCAmelCase    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )    ,						)




							def       snake_case			(					self						)		->       Union[str, Any]:
														# Initialize image_processing
														A      :					str      		=					self.image_processing_class(**self.image_processor_dict						)
														# create random numpy tensors
														A      :					Any      		=					prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=__UpperCAmelCase    ,						numpify=__UpperCAmelCase						)
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase    ,						np.ndarray						)

														# Test not batched input
														A      :					Tuple      		=					image_processing(image_inputs[0]    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )    ,						)

														# Test batched
														A      :					int      		=					image_processing(__UpperCAmelCase    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )    ,						)




							def       snake_case			(					self						)		->       List[Any]:
														# Initialize image_processing
														A      :					Tuple      		=					self.image_processing_class(**self.image_processor_dict						)
														# create random PyTorch tensors
														A      :					Tuple      		=					prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=__UpperCAmelCase    ,						torchify=__UpperCAmelCase						)
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase    ,						torch.Tensor						)

														# Test not batched input
														A      :					Optional[Any]      		=					image_processing(image_inputs[0]    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )    ,						)

														# Test batched
														A      :					Tuple      		=					image_processing(__UpperCAmelCase    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )    ,						)




							def       snake_case			(					self						)		->       Tuple:
														# Initialize image_processing
														A      :					str      		=					self.image_processing_class(**self.image_processor_dict						)
														# create random PyTorch tensors
														A      :					Tuple      		=					prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=__UpperCAmelCase    ,						torchify=__UpperCAmelCase						)
														A      :					int      		=					[]
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase    ,						torch.Tensor						)
																					maps.append(torch.zeros(image.shape[-2:]						).long()						)

														# Test not batched input
														A      :					str      		=					image_processing(image_inputs[0]    ,						maps[0]    ,						return_tensors='''pt'''						)
														self.assertEqual(
														    encoding['''pixel_values'''].shape    ,						(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )    ,						)
														self.assertEqual(
														    encoding['''labels'''].shape    ,						(
														        1,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )    ,						)
														self.assertEqual(encoding['''labels'''].dtype    ,						torch.long						)
														self.assertTrue(encoding['''labels'''].min().item() >= 0						)
														self.assertTrue(encoding['''labels'''].max().item() <= 2_55						)

														# Test batched
														A      :					List[Any]      		=					image_processing(__UpperCAmelCase    ,						__UpperCAmelCase    ,						return_tensors='''pt'''						)
														self.assertEqual(
														    encoding['''pixel_values'''].shape    ,						(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )    ,						)
														self.assertEqual(
														    encoding['''labels'''].shape    ,						(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )    ,						)
														self.assertEqual(encoding['''labels'''].dtype    ,						torch.long						)
														self.assertTrue(encoding['''labels'''].min().item() >= 0						)
														self.assertTrue(encoding['''labels'''].max().item() <= 2_55						)

														# Test not batched input (PIL images)
														A		,		A      :					Optional[Any]      		=					prepare_semantic_single_inputs()

														A      :					Tuple      		=					image_processing(__UpperCAmelCase    ,						__UpperCAmelCase    ,						return_tensors='''pt'''						)
														self.assertEqual(
														    encoding['''pixel_values'''].shape    ,						(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )    ,						)
														self.assertEqual(
														    encoding['''labels'''].shape    ,						(
														        1,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )    ,						)
														self.assertEqual(encoding['''labels'''].dtype    ,						torch.long						)
														self.assertTrue(encoding['''labels'''].min().item() >= 0						)
														self.assertTrue(encoding['''labels'''].max().item() <= 2_55						)

														# Test batched input (PIL images)
														A		,		A      :					Dict      		=					prepare_semantic_batch_inputs()

														A      :					Dict      		=					image_processing(__UpperCAmelCase    ,						__UpperCAmelCase    ,						return_tensors='''pt'''						)
														self.assertEqual(
														    encoding['''pixel_values'''].shape    ,						(
														        2,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )    ,						)
														self.assertEqual(
														    encoding['''labels'''].shape    ,						(
														        2,
														        self.image_processor_tester.crop_size['''height'''],
														        self.image_processor_tester.crop_size['''width'''],
														    )    ,						)
														self.assertEqual(encoding['''labels'''].dtype    ,						torch.long						)
														self.assertTrue(encoding['''labels'''].min().item() >= 0						)
														self.assertTrue(encoding['''labels'''].max().item() <= 2_55						)






							def       snake_case			(					self						)		->       Union[str, Any]:
														# Initialize image_processing
														A      :					int      		=					self.image_processing_class(**self.image_processor_dict						)

														# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
														A		,		A      :					str      		=					prepare_semantic_single_inputs()
														A      :					Any      		=					image_processing(__UpperCAmelCase    ,						__UpperCAmelCase    ,						return_tensors='''pt'''						)
														self.assertTrue(encoding['''labels'''].min().item() >= 0						)
														self.assertTrue(encoding['''labels'''].max().item() <= 1_50						)

														A      :					str      		=					True
														A      :					Union[str, Any]      		=					image_processing(__UpperCAmelCase    ,						__UpperCAmelCase    ,						return_tensors='''pt'''						)
														self.assertTrue(encoding['''labels'''].min().item() >= 0						)
														self.assertTrue(encoding['''labels'''].max().item() <= 2_55						)









import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
		import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin







@require_torch
class 					__lowercase  (						unittest.TestCase		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Optional[Any]      		=					load_tool('''text-to-speech'''						)
														self.tool.setup()




							def       snake_case			(					self						)		->       Union[str, Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					Optional[Any]      		=					self.tool('''hey'''						)
														A      :					List[str]      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






							def       snake_case			(					self						)		->       Optional[Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					int      		=					self.tool('''hey'''						)
														A      :					Any      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






